Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
There are many measures to report so-called treatment or causal effect: absolute difference,
ratio, odds ratio, number needed to treat, and so on. The choice of a measure, eg absolute …
ratio, odds ratio, number needed to treat, and so on. The choice of a measure, eg absolute …
Causal effects based on distributional distances
K Kim, J Kim, EH Kennedy - arXiv preprint arXiv:1806.02935, 2018 - arxiv.org
In this paper we develop a framework for characterizing causal effects via distributional
distances. In particular we define a causal effect in terms of the $ L_1 $ distance between …
distances. In particular we define a causal effect in terms of the $ L_1 $ distance between …
Identification and estimation of causal mechanisms and net effects of a treatment under unconfoundedness
CA Flores, A Flores-Lagunes - 2009 - papers.ssrn.com
An important goal when analyzing the causal effect of a treatment on an outcome is to
understand the mechanisms through which the treatment causally works. We define a …
understand the mechanisms through which the treatment causally works. We define a …
[引用][C] On the logic of collapsibility for causal effect measures
V Didelez, MJ Stensrud - Biometrical Journal, 2022 - Wiley Online Library
Liu et al.(2020) discuss the relation between efficacy measures within subgroups and
efficacy measures on the population level, which can be obtained by merging the …
efficacy measures on the population level, which can be obtained by merging the …
Approaches to treatment effect heterogeneity in the presence of confounding
SC Anoke, SL Normand, CM Zigler - Statistics in medicine, 2019 - Wiley Online Library
The literature on causal effect estimation tends to focus on the population mean estimand,
which is less informative as medical treatments are becoming more personalized and there …
which is less informative as medical treatments are becoming more personalized and there …
[PDF][PDF] Estimating Causal Effects by Bounding Confounding.
Assessing the causal effect of a treatment variable X on an outcome variable Y is usually
difficult due to the existence of unobserved common causes. Without further assumptions …
difficult due to the existence of unobserved common causes. Without further assumptions …
On a class of bias-amplifying variables that endanger effect estimates
J Pearl - arXiv preprint arXiv:1203.3503, 2012 - arxiv.org
This note deals with a class of variables that, if conditioned on, tends to amplify confounding
bias in the analysis of causal effects. This class, independently discovered by Bhattacharya …
bias in the analysis of causal effects. This class, independently discovered by Bhattacharya …
How balance and sample size impact bias in the estimation of causal treatment effects: a simulation study
Observational studies are often used to understand relationships between exposures and
outcomes. They do not, however, allow conclusions about causal relationships to be drawn …
outcomes. They do not, however, allow conclusions about causal relationships to be drawn …
Conditional separable effects
Researchers are often interested in treatment effects on outcomes that are only defined
conditional on posttreatment events. For example, in a study of the effect of different cancer …
conditional on posttreatment events. For example, in a study of the effect of different cancer …
Causal quartets: Different ways to attain the same average treatment effect
The average causal effect can often be best understood in the context of its variation. We
demonstrate with two sets of four graphs, all of which represent the same average effect but …
demonstrate with two sets of four graphs, all of which represent the same average effect but …